There have been proposed many techniques for filtering video image data in order to detect significant features of the image represented by the image data. Writings in this field include the following: M. P. Cagigal, et al., "Object movement characterization from low-light-level images", Optical Engineering, August 1994. vol. 33, no. 8, pp. 2810-2812; S. J. Nowlan, et al., "Filter selection model for motion segmentation and velocity integration," J. Opt. Soc. Am. A, December 1994, vol. 11, no. 12, pp. 3177-3200; T. G. Allen, et al., "Multiscale approaches to moving target detection in image sequences," Optical Engineering, July 1994, vol. 33, no. 7, pp. 2248-2254; M. -P. Dubuisson, et al., "Contour Extraction of Moving Objects in Complex Outdoor Scenes," International Journal of Computer Vision, 14, pp. 83-105 (1995); M. Bichsel, "Segmenting Simply Connected Moving Objects in a Static Scene," IEEE Transactions on Pattern Analysis and Machine Intelligence, Nov. 1994, vol. 16, no. 11, pp. 1138-1142; M. Irani, et al., "Computing Occluding and Transparent Motions," International Journal of Computer Vision, 12:1, pp. 5-16 (1994); I. J. Cox, "A Review of Statistical Data Association Techniques for Motion Correspondence," International Journal of Computer Vision, 10:1, pp. 53-66 (1993); D. Koller, et al., "Model-Based Object Tracking in Monocular Image Sequences of Road Traffic Scenes," International Journal of Computer Vision, 10:3, pp. 257-281 (1993); J. H. Lee, et al., "A VLSI Chip for motion estimation of HDTV Signals," IEEE Transactions on Consumer Electronics, May 1994, vol. 40, no. 2, pp. 154-160; T. Koivunen, "Motion detection of an interlaced video signal," IEEE Transactions on Consumer Electronics, August 1994, vol. 40, no. 3, pp. 753-760; S.-I. Jang, et al., "A real-time identification method on motion and out-of-focus blur for a video camera," IEEE Transactions on Consumer Electronics, May 1994, vol. 40, no. 2, pp. 145-153. However, commercial applications of these proposed filtering techniques have been rare, and for the most part have been limited to analysis of static images, rather than attempting to acquire information by analyzing a dynamic stream of images.
A product which makes significant use of information obtained by analysis of a stream of video images is the OPTIMA II video surveillance multiplexer introduced by the assignee of the present application. The OPTIMA II multiplexer receives a respective stream of video image information from each of a plurality of cameras and forms a combined stream of images by time-division multiplexing of the images from the cameras. The combined stream is then output to a conventional video tape recorder for storage on tape. The OPTIMA II multiplexer applies motion detection filtering to the respective input streams and adaptively allocates the "time slots" in the output stream by allocating a larger number of slots to images from an input stream in which motion is detected. In this way, an input stream having a relatively large amount of information is allocated a relatively large portion of the system's storage capacity.
The OPTIMA II multiplexer operates very effectively for its intended purpose and represents a significant advance. Further advances in management and use of the information contained in single or concurrent video image streams are also to be desired. It would be especially desirable to permit a user of a video surveillance system, or other system which stores dynamic image information, increased flexibility in the management of incoming video information and in the use of both incoming and stored video streams.